The following relates to the transportation arts, data processing arts, data analysis, tracking arts, and so forth.
Intelligent transportation systems generally include multiple vehicles, routes, and services that are utilized by a large number of users, which may include automatic ticketing validation systems that collect validation information for travelers. To aid management and planning of transportation systems it is desirable to be able to identify the origins and destinations of travelers. By identifying these end points, administrators are able to build and maintain more efficient transportation systems, such as by adding additional routes between frequently visited origins and destinations, increasing the number of buses or trains on a route, increasing the size of facilities (bus stops, train stations, etc.), and the like.
Transportation forecasting is a process of estimating a number of vehicles or people that will use a specific transportation facility in the future, e.g., a number of vehicles on a planned road or bridge, a number of passengers on a train, a number of passengers visiting an airport, a number of ships docking at a seaport, and the like. Transportation forecasting first begins with the collection of data on the current conditions or traffic at the facility (or a similar facility). This data is combined with other known information, e.g., population, employment, trip rates, costs, etc., which are then used to develop a traffic demand model for the current conditions, which may then be solved with predicted data to estimate future traffic conditions, e.g., amount of passengers on public transportation, number of vehicles on a bridge, etc. A typical transportation forecasting model includes trip generation, trip distribution, mode choice, and route assignment information.
Such estimated traffic forecasts may be used for transportation policy making, planning, engineering, and the like. For example, they may be used to determine a number of railway trains that should be available on a given route, a number of lanes on a proposed bridge, to estimate financial and social costs associated with a particular projection, to estimate possible environmental impacts, and the like.
Of particular use in transportation forecasting is the generation of origin-destination matrices during the trip distribution component. These matrices contain information about the spatial and temporal distribution of activities between different traffic zones in the network. Each cell of the matrix represents the number of passengers traveling between an origin and a destination in the study area. Origin-destination matrices are used to estimate the demand for transportation systems; then, based on anticipated future economic and population growth, land-use changes, and planning policies, these matrices are projected to identify and forecast future demand.
Real-time information collected by an automatic ticketing validation system may capture the traffic dynamics, and the dynamic traffic assignment may incorporate time dimension in the simulation of traffic flow. The time-dependent origin-destination is therefore one of the essential components of intelligent transportation system. However, previous attempts to generate these matrices from the real-time information have met with limited success. When assigned to the transportation system, these previous matrices failed to reproduce the observed traffic flows.
In particular, these previous attempts fail to identify and account for multi-goal trips of travelers on the transportation system. That is, in agglomeration areas with a dense public transportation network, travelers plan their trips with multiple goals in mind. Most typical examples are when travelers break or deviate from their trips on the way from home to the office or back. On their way to the office, a passenger may wish to accompany her kids to the school; on the way home, she may stop at a shopping mall, etc. Targeting multiple goals when using public transportation appears to be common for certain categories of travelers. The previous attempts at origin-destination matrix generation fail to account for these multiple goals, which negatively impacts the evaluation of any origin-destination matrix associated with the underlying transportation system. However, the identification of a transfer trip that targets one or multiple goals is a challenge.
Thus, it would be advantageous to provide a system and method capable of identifying multi-goal trips and appropriately incorporate these trips into a dynamic origin-destination matrix for a transportation system.